


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A			:						Union[str, Any]        =  {
    'configuration_clap': [
        'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ClapAudioConfig',
        'ClapConfig',
        'ClapTextConfig',
    ],
    'processing_clap': ['ClapProcessor'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						int        =  [
	    'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ClapModel',
	    'ClapPreTrainedModel',
	    'ClapTextModel',
	    'ClapTextModelWithProjection',
	    'ClapAudioModel',
	    'ClapAudioModelWithProjection',
	]
	A			:						Optional[int]        =  ['ClapFeatureExtractor']

if TYPE_CHECKING:
	from .configuration_clap import (
	    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
	    ClapAudioConfig,
	    ClapConfig,
	    ClapTextConfig,
	)
	from .processing_clap import ClapProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_clap import ClapFeatureExtractor
		from .modeling_clap import (
		    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ClapAudioModel,
		    ClapAudioModelWithProjection,
		    ClapModel,
		    ClapPreTrainedModel,
		    ClapTextModel,
		    ClapTextModelWithProjection,
		)


else:
	import sys

	A			:						Union[str, Any]        =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	str     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	str   )	->       Any:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	int     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	Optional[torch.Generator] = None     , UpperCAmelCase							:	int = 50     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , **UpperCAmelCase							:	Dict     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :Optional[Any]						=			torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     , generator=UpperCAmelCase     , )
											lowerCAmelCase   :str						=			image.to(self.device   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase   )

											for t in self.progress_bar(self.scheduler.timesteps   ):
																	# 1. predict noise model_output
																	lowerCAmelCase   :Dict						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample

																	# 2. predict previous mean of image x_t-1 and add variance depending on eta
																	# eta corresponds to η in paper and should be between [0, 1]
																	# do x_t -> x_t-1
																	lowerCAmelCase   :List[str]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

											lowerCAmelCase   :Any						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :Tuple						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :Dict						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,), "This is a local test"

											return ImagePipelineOutput(images=UpperCAmelCase   ), "This is a local test"